Lifeboat Foundation

Nice.

Networks are mathematical representations to explore and understand diverse, complex systems—everything from military logistics and global finance to air traffic, social media, and the biological processes within our bodies. In each of those systems, a hierarchy of recurring, meaningful internal patterns—such as molecules and proteins interacting inside cells, and capacitors and resistors operating within integrated circuits—determines the functions or behaviors of those systems. The larger and more intricate a system is, however, the harder it is for current network modeling techniques to uncover these patterns and represent them in organized, easy-to-understand ways.

Researchers at Stanford University, funded by DARPA’s Simplifying Complexity in Scientific Discovery (SIMPLEX) program, have made progress in overcoming these challenges through a framework they have developed for identifying and clustering what mathematicians call “motifs”: essential but often obscure patterns within systems that are the building blocks of mathematical modeling and that facilitate the computational representation of complex systems.

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An example of synthetic biology is making ‘real’ milk without rearing cows.

As we continue to improve cell circuitry, we will see this is going to be more and more important to our tech future. I believe once we have the underlying infrastructure improved with QC that we will see more advancement made in Biocomputing as well as opportunities to adopt on multiple levels including Singularity.

Cells that are electrically active and that also produce light for easy voltage monitoring could lead to new studies of heart arrhythmias and possibly bio-computing.

The human heartbeat is produced by electrical pulses that propagate through cardiac tissue, causing rhythmic muscle contraction. Researchers have previously engineered cells to create an artificial tissue capable of producing coordinated electrical activity, and now a team has added the ability to monitor their electrical state by detecting fluorescent emission. They have also fashioned the cells into “living circuits” that might act as model systems for studying heart behavior.

It may seem like something from science fiction, but researchers have found a group of microorganisms that can live off of pure electricity, reports. All life uses electricity, but scientists long thought it impossible for a cell to directly consume and expel electrons. That’s because fatty cell membranes act as insulators, preventing the flow of electricity. Scientists have now found evidence that some cells can discharge electrons through specialized proteins in their membranes, and others can ingest electrons from an electrode by using an enzyme that creates hydrogen atoms. Still others might be able to directly consume electrons, though that research has yet to be published. The findings could help researchers understand how life thrives under a variety of conditions, and how it could exist on places like Mars.

Mobile phones have become commonplace. Modern communication devices like mobile phones need to exchange huge amounts of information. However, what is hidden underneath the elegantly shaped plastic casings is quickly forgotten: Complex signal processors constantly fighting against noise and steadily adapting themselves to changing environment.

But noise and changing environmental conditions do not only affect electrical circuits. In synthetic biology scientists are facing similar problems. However, in synthetic biology a methodology to deal with noise does not exist yet. Prof. Mustafa Khammash and Christoph Zechner of the Department of Biosystems Science and Engineering have studied how conventional signal processors can be translated into biochemical processes — built and operated inside living cells.

A major limitation in engineering biological circuits is that host cells — even if they are genetically identical — are never the same. For instance, cell A might be in a different cell-cycle stage or have more ribosomes available than cell B. Therefore, the same synthetic circuit may behave very differently in each of these two cells. In extreme cases, only a small fraction of cells might show the correct behavior, while the remaining cells act unpredictably. This is referred to as context-dependency.

There’s a saying among futurists that a human-equivalent artificial intelligence will be our last invention. After that, AIs will be capable of designing virtually anything on their own — including themselves. Here’s how a recursively self-improving AI could transform itself into a superintelligent machine.

When it comes to understanding the potential for artificial intelligence, it’s critical to understand that an AI might eventually be able to modify itself, and that these modifications could allow it to increase its intelligence extremely fast.

Once sophisticated enough, an AI will be able to engage in what’s called “recursive self-improvement.” As an AI becomes smarter and more capable, it will subsequently become better at the task of developing its internal cognitive functions. In turn, these modifications will kickstart a cascading series of improvements, each one making the AI smarter at the task of improving itself. It’s an advantage that we biological humans simply don’t have.

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IARPA is hosting a Proposers’ Day for businesses in a couple weeks.

For robots; the bigger question where is the bigger ROI? Robots trying to be built to out do people; or is it better to enhance people? DARPA is more focused on enhancing people such as soldiers; and I agree with DARPA.

Understanding the hierarchical structure of biological networks like human brain — a network of neurons — could be useful in creating more complex, intelligent computational brains in the fields of artificial intelligence and robotics, says a study.

Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, a…

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Researchers at the University of Manchester, UK have made the first autonomous chemically powered synthetic small-molecule motor. The new device, which is very much like the protein motors found in biological cells, might be used to design artificial molecular machines similar to those found in nature. Such machines could be important for applications such as synthetic muscles, nano- and micro-robots and advanced mechanical motors.

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